GCN training.
In spite of their widespread success in several graph related tasks, GCNs often suffer from training inefficiencies [1]}, [2]}.
Consequently, the research community has focused on developing efficient and scalable algorithms for training GCNs [3]}, [4]}, [5]}, [6]}, [7]}, [8]}.
The resulting approaches can be divided roughly into two areas: neighborhood sampling and graph partitioning.
However, it is important to note that these two broad classes of solutions are not mutually exclusive, and reasonable combinations of the two approaches may be beneficial.
